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Title: Detecting stock-price manipulation in an emerging market: The case of Turkey
Authors: Ögüt, Hulisi
Doğanay, M. Mete
Aktaş, Ramazan
Keywords: Stock market
Data mining techniques
Multivariate statistical techniques
Issue Date: 2009
Publisher: Pergamon-Elsevier Science Ltd
Abstract: This paper aims to develop methods that are capable of detecting manipulation in the Istanbul Stock Exchange. We take the difference between manipulated stock's and index's average daily return, average daily change in trading volume and average daily volatility and used these statistics as explanatory variables. The data in post-manipulation and pre-manipulation periods are used as non-manipulated instances while the data in the manipulation period are used as manipulated instances. Test performance of classification accuracy, sensitivity and specificity statistics for Artificial Neural Networks (ANN) and Support Vector Machine (SVM) are compared with the results of discriminant analysis and logistics regression (logit). We found that the data mining techniques (ANN and SVM) are better suited to detect stock-price manipulation than multivariate statistical techniques (discriminant analysis, logistics regression) as the performances of the data mining techniques in terms of total classification accuracy and sensitivity statistics are better than those of multivariate techniques. We also found that unit change in difference between average daily return of manipulated stock and the index has the largest effect while unit change in difference between average daily change in trading volume of manipulated stock and index has the least effect on multivariate classifiers' decision functions. (C) 2009 Elsevier Ltd. All rights reserved.
ISSN: 0957-4174
Appears in Collections:İşletme Bölümü / Department of Management
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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